Об этом курсе: How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.
Автор: University of Pennsylvania
Преподаватели: Daniel Lee, Professor of Electrical and Systems Engineering
School of Engineering and Applied Science
| Основные сведения | Курс 5 из 6 — Robotics Specialization |
| Выполнение | 4 weeks of study, 3-4 hours/week |
| Язык | English, Субтитры: Chinese (Simplified) |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Gaussian Model Learning
We will learn about the Gaussian distribution for parametric modeling in robotics. The Gaussian distribution is the most widely used continuous distribution and provides a useful way to estimate uncertainty and predict in the world. We will start by discussing the one-dimensional Gaussian distribution, and then move on to the multivariate Gaussian distribution. Finally, we will extend the concept to models that use Mixtures of Gaussians.
9 видео, 3 материала для самостоятельного изучения
- Reading: MATLAB Tutorial - Getting Started with MATLAB
- Reading: Setting Up your MATLAB Environment
- Reading: Basic Probability
- Видео: WEEK 1 Introduction
- Видео: 1.2.1. 1D Gaussian Distribution
- Видео: 1.2.2. Maximum Likelihood Estimate (MLE)
- Видео: 1.3.1. Multivariate Gaussian Distribution
- Видео: 1.3.2. MLE of Multivariate Gaussian
- Видео: 1.4.1. Gaussian Mixture Model (GMM)
- Видео: 1.4.2. GMM Parameter Estimation via EM
- Видео: 1.4.3. Expectation-Maximization (EM)
Оцениваемый: Color Learning and Target Detection
НЕДЕЛЯ 2
Bayesian Estimation - Target Tracking
We will learn about the Gaussian distribution for tracking a dynamical system. We will start by discussing the dynamical systems and their impact on probability distributions. This linear Kalman filter system will be described in detail, and, in addition, non-linear filtering systems will be explored.
5 видео
- Видео: Kalman Filter Motivation
- Видео: System and Measurement Models
- Видео: Maximum-A-Posterior Estimation
- Видео: Extended Kalman Filter and Unscented Kalman Filter
Оцениваемый: Kalman Filter Tracking
НЕДЕЛЯ 3
Mapping
We will learn about robotic mapping. Specifically, our goal of this week is to understand a mapping algorithm called Occupancy Grid Mapping based on range measurements. Later in the week, we introduce 3D mapping as well.
6 видео
- Видео: Introduction to Mapping
- Видео: 3.2.1. Occupancy Grid Map
- Видео: 3.2.2. Log-odd Update
- Видео: 3.2.3. Handling Range Sensor
- Видео: Introduction to 3D Mapping
Оцениваемый: 2D Occupancy Grid Mapping
НЕДЕЛЯ 4
Bayesian Estimation - Localization
We will learn about robotic localization. Specifically, our goal of this week is to understand a how range measurements, coupled with odometer readings, can place a robot on a map. Later in the week, we introduce 3D localization as well.
6 видео
- Видео: Odometry Modeling
- Видео: Map Registration
- Видео: Particle Filter
- Видео: Iterative Closest Point
- Видео: Closing
Оцениваемый: Particle Filter Based Localization
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Авторы
University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Стоимость
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| Получить итоговую оценку | Доступен |
| Получите сертификат о прохождении курса, ссылкой на который можно делиться с другими людьми | Доступен |
Рейтинги и отзывы
The assignment is not designed very well especially the last one. Lacking of lots of details.
First 2 Week was very good but I think Mapping and Localization parts were not covered throughly. There should have been more detail.
Good materials for beginners. Assignments are interesting and useful.
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good course ..expecting more follow up courses on this topic !